Association of Metabolic Comorbidities With Fibrosis Severity and Fibrosis Regression in Patients With Chronic Hepatitis B
Bibliographic record
Abstract
BACKGROUND & AIMS: The presence of metabolic comorbidities is associated with a higher risk of liver-related events in chronic hepatitis B (CHB) patients. However, the association between presence of metabolic comorbidities and the severity of biopsy-proven liver fibrosis is yet unknown. METHODS: Data from CHB patients from 2 tertiary clinics and 8 clinical trials was analyzed. We studied the association between presence of metabolic comorbidities with severity of liver fibrosis in untreated patients, and with fibrosis regression or progression in biopsies taken after initiation of antiviral therapy. RESULTS: We analyzed biopsies from 3179 untreated CHB patients. Median age was 37 years, 57.6% were hepatitis B e antigen positive, with median hepatitis B virus DNA of 7.30 logIU/mL. Overweight (29.4% vs 19.0%; P < .001), hypertension (40.7% vs 23.2%; P < .001), diabetes (42.2% vs 23.6%; P < .001), and dyslipidemia (42.9 vs 23.6%; P < .001) were associated with a higher risk of advanced fibrosis, with the highest risk observed in patients with multiple comorbidities. Findings were consistent in multivariable analysis (1 comorbidity: adjusted odds ratio [aOR], 1.115; ≥2 comorbidities: aOR, 1.627; P = .006). Regression to nonadvanced fibrosis, after treatment initiation, was more often observed in patients without metabolic comorbidities (43.1%), compared with patients with 1 (31.6%) or ≥2 comorbidities (17.0%) (P = .005). Findings were consistent in multivariable analysis (1 comorbidity: aOR, 0.792; ≥2 comorbidities: aOR, 0.260; P = .025). The risk of progression to advanced fibrosis was highest in patients with ≥2 comorbidities (14.3% vs 4.6%; P = .001). CONCLUSIONS: Presence of metabolic comorbidities in untreated CHB patients is associated with more severe liver fibrosis and, after initiation of antiviral therapy, with less fibrosis regression and a higher risk of fibrosis progression.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".